A deep-learning based gait classification and anomaly detection framework for healthcare surveillance

Gait classification and anomaly detection are non-intrusive approaches that can support healthcare surveillance and clinical gait analysis by identifying abnormal walking patterns. Despite recent advancements, existing methods remain limited by model complexity, high computational requirements, and privacy concerns. This study proposes a unified framework that combines three complementary components: (i) transformer-based temporal modeling to capture both short-term and long-term gait dynamics, (ii) lightweight architectures for efficient deployment on edge devices, and (iii) federated learning (FL) for privacy-preserving distributed training without requiring raw data sharing. Experiments were conducted on a balanced subset of 60,000 images from the Gait Detection Processed dataset using a controlled federated learning environment designed as a proof-of-concept evaluation rather than a large-scale deployment setting. The dataset consisted of three categories: background/non-gait, normal gait, and abnormal gait. Vision Transformer (ViT), ConvLSTM, and MobileViT architectures were evaluated for three-class gait classification and anomaly detection under a federated learning setting. Among the evaluated models, MobileViT-Large achieved the highest performance with 97.2% accuracy, 96.8% precision, 97.5% recall, and 97.1% F1-score, although it required higher computational resources and showed greater overfitting tendencies. MobileViT-Small achieved the best balance between efficiency and performance with 94.0% accuracy, making it more suitable for edge deployment. SHAP-based analysis further showed that the models focused on meaningful gait regions, such as torso and limb movements. This proposed framework provides a comparative benchmark of recurrent and transformer-based architectures within a privacy-preserving framework for healthcare monitoring applications.